Motivated by recent atomic-action datasets ([1]}, [2]}, [3]}, [4]}) and the significant advances in fine-grained action recognition (AR) the utilisation of video data captured within the shared work environment is now an attractive, unintrusive, and information rich approach to classifying fundamental atomic actions between a worker, product, part or tool. With the accurate classification of these actions allowing assemblies to be understood as a nested set of fundamental atomic actions. Whilst many works have focused on classifying actions within manufacturing environments, most of these works have been limited in their approach; often operating within a controlled environment, classifying coarse actions with single frames, or performing action segmentation in an offline manner. In contrast, many HRC systems require real time classification of spatially and temporally similar fine-grained actions, which is often very challenging due to the spatial limitations of AR inputs (224x224) where often significant downsampling is used – removing vital information collected by the camera.
